In recent years, utility customers have become more conscious of their power consumption. As their average consumption has increased, loads on electricity distribution grids have likewise grown. Utility providers must provide power to these increased loads by increasing production capacity or by more intelligently managing the consumption of consumers with incentive programs, pricing schedules, and related concepts.
Load on the grid fluctuates over the course of a day. Because it is expensive for utility providers to provide high power levels for short periods of time, utilities often define certain times of the day as “peak” periods and “off-peak” periods and then charge customers more for electricity consumed during the peak periods. Utilities have also charged customers with “peak demand charges” that are not based primarily on the time of day, but are instead are assessed at a value proportional to the magnitude of the highest peak in power consumption (e.g., in kilowatts (kW)) that the customer incurs during a period of time, such as over the course of a billing period. Peak demand charges can be a substantial burden, especially for customers that use devices and appliances that draw high power loads over short periods of time. For example, peak demand charges are especially high for businesses and residences that have electric vehicle (EV) charging stations, large freezers, machinery, or large heating, ventilation, and air conditioning (HVAC) units.
Consumers have sought ways to limit and control their peak demand charges, including by implementing devices and software for controlling the timing and power levels of operation of their electricity consuming devices (i.e., load shedding) and/or providing energy to the consumer from alternate sources (e.g., storage batteries, fuel cells, or generators) when metered power levels exceed a predetermined “setpoint” value. Each of these systems may be generically referred to as a consumption management system (CMS) since they manage the consumption of the electricity customer. In one example CMS, a high-capacity battery is connected to the site via a high-power inverter. A computer system monitors the metered power consumption of the customer and detects when the power levels reach the customer's setpoint, such as when a brief spike or peak in consumption occurs that would result in an increased demand charge for the customer. In response, the CMS allows the site to draw energy from the battery via the inverter to supplement or supplant the utility grid connection. The net consumption is measured at the utility meter, rather than the actual consumption, so the utility provider detects a lower peak consumption than would otherwise be registered. A lower registered peak corresponds with a lower peak demand charge for that billing period, provided that the consumption of the site does not spike above the setpoint for the rest of the billing period without being mitigated by the CMS.
A large-capacity CMS is expensive for most customers, so the CMS is usually designed with lower specifications to reduce costs. If the CMS costs far more than the money it would save in peak demand charges, it is typically not desirable to the customer. Further complications may also arise because the performance of a given CMS may be difficult to predict. Load prediction algorithms have been devised to help the CMS decide when and how much to charge or discharge the battery, but they are imperfect due to uncertainty in when and how large spikes or peaks in consumption may occur. In some cases, the performance of a CMS may not be known until after it has already been implemented and purchased by the customer. Thus, the overall cost and uncertainty in results limits the adoption rate of these systems despite their potentially great benefits to consumers.
Accordingly, there is a need for improvements in the methods and systems used for determining the feasibility and optimal implementation of consumption management systems.